An Initial Framework for Mobile Healthcare Systems using Deep Neural Networks

Abstract

Disease prevention and health management is becoming the key focus of diagnostic medicine these days rather than focusing on treatment after the infliction. Research advancements in using Artificial Intelligence for healthcare has enabled personalized diagnostic and treatment applications for disease identification, management and prevention. What was previously limited due to vast amount of data has now been possible because of Deep Neural Networks and their application for modeling complex diagnostic decisions. Mobile healthcare applications powered by Deep Neural Networks has enabled people to triage their conditions and make preemptive treatment decisions. In this paper, we reviewed recent advancements done in mobile healthcare applications using deep learning in the past 2–3 years and classified these implementations into three major categories: Cloud-computing assisted mobile healthcare systems, Edge-computing assisted mobile healthcare systems and offline mobile healthcare systems. Furthermore, based on the recent literature, we identified an initial framework that most mobile healthcare applications using deep learning employ.